Elena Kochkina

Opinion Mining Using Heterogeneous Online Data

Understanding public opinion is important in many applications, such as improving company's product or service, marketing research, recommendation systems, decision and policy making and even predicting results of elections. Social media is a very powerful tool to transfer information and express emotions for users and a rich source of data that enables researchers to mine public opinion.
In this talk I will describe different lines of work within our group, including rumour stance and veracity classification classification, predicting well-being based on heterogeneous user generated data and target-dependent sentiment recognition. False information circulating on social media presents many risks as social media is used as a source of news by many users. Detecting rumourous content is important to prevent the spread of false information which can affect important decisions and stock markets. Rumour stance classification is considered to be an important step towards rumour verification as claims that attract a lot of scepticism among users are more likely to be proven false later. Therefore performing stance classification well is expected to be useful in debunking false rumours. In our work we classify a set of Twitter posts from conversations discussing rumours as either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that achieves state-of-the-art results on this task through modelling the conversational structure of tweets. The task of automatically assessing well-being using smartphones and online social media is becoming of crucial importance, as an attempt to help individuals self-monitor their mental health state. In the current work, a multiple kernel learning approach is proposed as a mental health predictor, trained on heterogeneous (text and smartphone) user-generated data. The results reveal the efficiency of the proposed model and sequential approaches for time series modelling (i.e., LSTMs) are proposed for future work. Opinion mining is usually achieved by determining the overall sentiment expressed. However, inferring the sentiment towards specific targets is limited by such an approach since a Social Media posts may contain different types of sentiment expressed towards each of the targets mentioned. Our work on target-specific sentiment recognition goes beyond tweet-level or single-target approaches, and proposes a multi-target-specific sentiment classification model, which explores the context around a target as well as syntactic dependencies involving the target.

I am a Computer Science PhD student at the University of Warwick supervised by Dr. Maria Liakata and Prof. Rob Procter. Currently I am based at the Alan Turing Institute in London. My background is Applied mathematics and Complexity science. I work in the area of Natural Language Processing. My research is focused on Rumour Stance and Veracity Classification in Twitter conversations. I am studying the benefits of utilising the conversation structure in supervised learning models.